Overview

Brought to you by YData

Dataset statistics

Number of variables64
Number of observations379
Missing cells2580
Missing cells (%)10.6%
Total size in memory189.6 KiB
Average record size in memory512.3 B

Variable types

Numeric29
DateTime21
Unsupported1
Text13

Alerts

Time has 218 (57.5%) missing values Missing
State has 4 (1.1%) missing values Missing
Position.3 has 9 (2.4%) missing values Missing
Position.5 has 11 (2.9%) missing values Missing
Position.6 has 19 (5.0%) missing values Missing
Position.7 has 18 (4.7%) missing values Missing
Position.8 has 17 (4.5%) missing values Missing
Position.9 has 17 (4.5%) missing values Missing
Position.10 has 20 (5.3%) missing values Missing
Position.11 has 23 (6.1%) missing values Missing
Position.12 has 29 (7.7%) missing values Missing
Position.14 has 34 (9.0%) missing values Missing
Position.15 has 38 (10.0%) missing values Missing
Position.16 has 37 (9.8%) missing values Missing
Green Gate has 7 (1.8%) missing values Missing
Position.17 has 42 (11.1%) missing values Missing
Auburn Lake Trails has 98 (25.9%) missing values Missing
Position.18 has 46 (12.1%) missing values Missing
Quarry Road has 178 (47.0%) missing values Missing
Position.19 has 46 (12.1%) missing values Missing
Pointed Rocks has 204 (53.8%) missing values Missing
Position.20 has 46 (12.1%) missing values Missing
Robie Point has 219 (57.8%) missing values Missing
Position.21 has 49 (12.9%) missing values Missing
Finish has 219 (57.8%) missing values Missing
Position.22 has 50 (13.2%) missing values Missing
Finish_time has 219 (57.8%) missing values Missing
Robie Point_time has 219 (57.8%) missing values Missing
Robie Point_GAP has 219 (57.8%) missing values Missing
Finish_GAP has 219 (57.8%) missing values Missing
Overall Place has unique values Unique
Position.2 has unique values Unique
Bib is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-01-19 21:21:26.655196
Analysis finished2025-01-19 21:21:27.445496
Duration0.79 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Overall Place
Real number (ℝ)

Unique 

Distinct379
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190
Minimum1
Maximum379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:27.605881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.9
Q195.5
median190
Q3284.5
95-th percentile360.1
Maximum379
Range378
Interquartile range (IQR)189

Descriptive statistics

Standard deviation109.5521185
Coefficient of variation (CV)0.5765900973
Kurtosis-1.2
Mean190
Median Absolute Deviation (MAD)95
Skewness0
Sum72010
Variance12001.66667
MonotonicityStrictly increasing
2025-01-19T22:21:27.792157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
250 1
 
0.3%
259 1
 
0.3%
258 1
 
0.3%
257 1
 
0.3%
256 1
 
0.3%
255 1
 
0.3%
254 1
 
0.3%
253 1
 
0.3%
252 1
 
0.3%
Other values (369) 369
97.4%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
379 1
0.3%
378 1
0.3%
377 1
0.3%
376 1
0.3%
375 1
0.3%

Time
Date

Missing 

Distinct110
Distinct (%)68.3%
Missing218
Missing (%)57.5%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 23:57:22
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:28.041618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:28.256194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Bib
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.1 KiB
Distinct264
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:28.732141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length9
Mean length5.503957784
Min length2

Characters and Unicode

Total characters2086
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique204 ?
Unique (%)53.8%

Sample

1st rowTom
2nd rowTyler
3rd rowAnthony
4th rowJiasheng
5th rowDaniel
ValueCountFrequency (%)
scott 7
 
1.8%
john 7
 
1.8%
brian 6
 
1.6%
michael 6
 
1.6%
matt 5
 
1.3%
daniel 5
 
1.3%
david 5
 
1.3%
ryan 5
 
1.3%
jennifer 4
 
1.0%
matthew 4
 
1.0%
Other values (258) 329
85.9%
2025-01-19T22:21:29.306677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 227
 
10.9%
a 222
 
10.6%
n 167
 
8.0%
i 159
 
7.6%
r 126
 
6.0%
l 104
 
5.0%
o 101
 
4.8%
t 98
 
4.7%
h 77
 
3.7%
s 68
 
3.3%
Other values (43) 737
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2086
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 227
 
10.9%
a 222
 
10.6%
n 167
 
8.0%
i 159
 
7.6%
r 126
 
6.0%
l 104
 
5.0%
o 101
 
4.8%
t 98
 
4.7%
h 77
 
3.7%
s 68
 
3.3%
Other values (43) 737
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2086
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 227
 
10.9%
a 222
 
10.6%
n 167
 
8.0%
i 159
 
7.6%
r 126
 
6.0%
l 104
 
5.0%
o 101
 
4.8%
t 98
 
4.7%
h 77
 
3.7%
s 68
 
3.3%
Other values (43) 737
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2086
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 227
 
10.9%
a 222
 
10.6%
n 167
 
8.0%
i 159
 
7.6%
r 126
 
6.0%
l 104
 
5.0%
o 101
 
4.8%
t 98
 
4.7%
h 77
 
3.7%
s 68
 
3.3%
Other values (43) 737
35.3%
Distinct359
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:29.618844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length12
Mean length6.422163588
Min length2

Characters and Unicode

Total characters2434
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique345 ?
Unique (%)91.0%

Sample

1st rowEvans
2nd rowGreen
3rd rowCostales
4th rowShen
5th rowJones
ValueCountFrequency (%)
jones 4
 
1.0%
garcia 3
 
0.8%
davis 3
 
0.8%
watson 3
 
0.8%
cook 3
 
0.8%
campbell 2
 
0.5%
van 2
 
0.5%
smith 2
 
0.5%
white 2
 
0.5%
scott 2
 
0.5%
Other values (353) 358
93.2%
2025-01-19T22:21:30.128238image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 230
 
9.4%
a 226
 
9.3%
n 193
 
7.9%
o 183
 
7.5%
r 182
 
7.5%
i 140
 
5.8%
l 118
 
4.8%
s 108
 
4.4%
t 105
 
4.3%
h 79
 
3.2%
Other values (45) 870
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 230
 
9.4%
a 226
 
9.3%
n 193
 
7.9%
o 183
 
7.5%
r 182
 
7.5%
i 140
 
5.8%
l 118
 
4.8%
s 108
 
4.4%
t 105
 
4.3%
h 79
 
3.2%
Other values (45) 870
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 230
 
9.4%
a 226
 
9.3%
n 193
 
7.9%
o 183
 
7.5%
r 182
 
7.5%
i 140
 
5.8%
l 118
 
4.8%
s 108
 
4.4%
t 105
 
4.3%
h 79
 
3.2%
Other values (45) 870
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 230
 
9.4%
a 226
 
9.3%
n 193
 
7.9%
o 183
 
7.5%
r 182
 
7.5%
i 140
 
5.8%
l 118
 
4.8%
s 108
 
4.4%
t 105
 
4.3%
h 79
 
3.2%
Other values (45) 870
35.7%

Gender
Text

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:30.235173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters379
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM
ValueCountFrequency (%)
m 296
78.1%
f 83
 
21.9%
2025-01-19T22:21:30.508873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 296
78.1%
F 83
 
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 296
78.1%
F 83
 
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 296
78.1%
F 83
 
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 296
78.1%
F 83
 
21.9%

Age
Real number (ℝ)

Distinct47
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.04485488
Minimum21
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:30.730303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile30.9
Q138
median45
Q351
95-th percentile61
Maximum75
Range54
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.400866428
Coefficient of variation (CV)0.2087001158
Kurtosis-0.3176776947
Mean45.04485488
Median Absolute Deviation (MAD)6
Skewness0.1634995131
Sum17072
Variance88.3762896
MonotonicityNot monotonic
2025-01-19T22:21:30.921106image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
41 21
 
5.5%
47 21
 
5.5%
49 19
 
5.0%
44 17
 
4.5%
32 17
 
4.5%
42 17
 
4.5%
48 15
 
4.0%
45 15
 
4.0%
39 14
 
3.7%
38 14
 
3.7%
Other values (37) 209
55.1%
ValueCountFrequency (%)
21 1
 
0.3%
25 1
 
0.3%
26 1
 
0.3%
27 2
 
0.5%
28 7
1.8%
ValueCountFrequency (%)
75 1
0.3%
70 1
0.3%
69 1
0.3%
67 1
0.3%
66 2
0.5%

City
Text

Distinct308
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:31.270451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length26
Median length17
Mean length8.868073879
Min length2

Characters and Unicode

Total characters3361
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique265 ?
Unique (%)69.9%

Sample

1st rowLoughborough
2nd rowPortland
3rd rowSalt Lake City
4th rowKunming
5th rowWellington
ValueCountFrequency (%)
city 13
 
2.6%
san 12
 
2.4%
lake 10
 
2.0%
boulder 7
 
1.4%
auburn 7
 
1.4%
salt 6
 
1.2%
beach 6
 
1.2%
springs 5
 
1.0%
valley 5
 
1.0%
london 4
 
0.8%
Other values (342) 429
85.1%
2025-01-19T22:21:31.768778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 335
 
10.0%
e 282
 
8.4%
o 259
 
7.7%
n 221
 
6.6%
r 205
 
6.1%
l 193
 
5.7%
i 169
 
5.0%
t 159
 
4.7%
s 141
 
4.2%
125
 
3.7%
Other values (47) 1272
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3361
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 335
 
10.0%
e 282
 
8.4%
o 259
 
7.7%
n 221
 
6.6%
r 205
 
6.1%
l 193
 
5.7%
i 169
 
5.0%
t 159
 
4.7%
s 141
 
4.2%
125
 
3.7%
Other values (47) 1272
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3361
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 335
 
10.0%
e 282
 
8.4%
o 259
 
7.7%
n 221
 
6.6%
r 205
 
6.1%
l 193
 
5.7%
i 169
 
5.0%
t 159
 
4.7%
s 141
 
4.2%
125
 
3.7%
Other values (47) 1272
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3361
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 335
 
10.0%
e 282
 
8.4%
o 259
 
7.7%
n 221
 
6.6%
r 205
 
6.1%
l 193
 
5.7%
i 169
 
5.0%
t 159
 
4.7%
s 141
 
4.2%
125
 
3.7%
Other values (47) 1272
37.8%

State
Text

Missing 

Distinct83
Distinct (%)22.1%
Missing4
Missing (%)1.1%
Memory size3.1 KiB
2025-01-19T22:21:31.998165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length2
Mean length2.458666667
Min length2

Characters and Unicode

Total characters922
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)11.5%

Sample

1st rowLeicestershire
2nd rowOR
3rd rowUT
4th rowCHN
5th rowCO
ValueCountFrequency (%)
ca 94
24.8%
co 26
 
6.9%
gbr 17
 
4.5%
or 15
 
4.0%
wa 14
 
3.7%
ut 12
 
3.2%
az 12
 
3.2%
tx 12
 
3.2%
mn 9
 
2.4%
ma 7
 
1.8%
Other values (77) 161
42.5%
2025-01-19T22:21:32.404275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 165
17.9%
C 139
15.1%
O 59
 
6.4%
N 54
 
5.9%
R 42
 
4.6%
T 41
 
4.4%
M 33
 
3.6%
B 27
 
2.9%
W 26
 
2.8%
I 26
 
2.8%
Other values (38) 310
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 165
17.9%
C 139
15.1%
O 59
 
6.4%
N 54
 
5.9%
R 42
 
4.6%
T 41
 
4.4%
M 33
 
3.6%
B 27
 
2.9%
W 26
 
2.8%
I 26
 
2.8%
Other values (38) 310
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 165
17.9%
C 139
15.1%
O 59
 
6.4%
N 54
 
5.9%
R 42
 
4.6%
T 41
 
4.4%
M 33
 
3.6%
B 27
 
2.9%
W 26
 
2.8%
I 26
 
2.8%
Other values (38) 310
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 165
17.9%
C 139
15.1%
O 59
 
6.4%
N 54
 
5.9%
R 42
 
4.6%
T 41
 
4.4%
M 33
 
3.6%
B 27
 
2.9%
W 26
 
2.8%
I 26
 
2.8%
Other values (38) 310
33.6%
Distinct30
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:32.572510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1137
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)4.7%

Sample

1st rowGBR
2nd rowUSA
3rd rowUSA
4th rowCHN
5th rowNZL
ValueCountFrequency (%)
usa 293
77.3%
gbr 20
 
5.3%
can 11
 
2.9%
aus 8
 
2.1%
fra 6
 
1.6%
swe 4
 
1.1%
jpn 4
 
1.1%
ita 4
 
1.1%
chn 4
 
1.1%
nzl 3
 
0.8%
Other values (20) 22
 
5.8%
2025-01-19T22:21:32.856039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 325
28.6%
S 310
27.3%
U 306
26.9%
R 33
 
2.9%
N 27
 
2.4%
G 23
 
2.0%
B 20
 
1.8%
C 18
 
1.6%
E 10
 
0.9%
P 9
 
0.8%
Other values (14) 56
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 325
28.6%
S 310
27.3%
U 306
26.9%
R 33
 
2.9%
N 27
 
2.4%
G 23
 
2.0%
B 20
 
1.8%
C 18
 
1.6%
E 10
 
0.9%
P 9
 
0.8%
Other values (14) 56
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 325
28.6%
S 310
27.3%
U 306
26.9%
R 33
 
2.9%
N 27
 
2.4%
G 23
 
2.0%
B 20
 
1.8%
C 18
 
1.6%
E 10
 
0.9%
P 9
 
0.8%
Other values (14) 56
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 325
28.6%
S 310
27.3%
U 306
26.9%
R 33
 
2.9%
N 27
 
2.4%
G 23
 
2.0%
B 20
 
1.8%
C 18
 
1.6%
E 10
 
0.9%
P 9
 
0.8%
Other values (14) 56
 
4.9%
Distinct367
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1900-01-01 01:45:35
Maximum1900-01-01 04:25:53
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:33.035616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:33.286791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.2
Real number (ℝ)

Unique 

Distinct379
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190
Minimum1
Maximum379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:33.508377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.9
Q195.5
median190
Q3284.5
95-th percentile360.1
Maximum379
Range378
Interquartile range (IQR)189

Descriptive statistics

Standard deviation109.5521185
Coefficient of variation (CV)0.5765900973
Kurtosis-1.2
Mean190
Median Absolute Deviation (MAD)95
Skewness0
Sum72010
Variance12001.66667
MonotonicityNot monotonic
2025-01-19T22:21:33.715126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 1
 
0.3%
160 1
 
0.3%
320 1
 
0.3%
164 1
 
0.3%
189 1
 
0.3%
310 1
 
0.3%
193 1
 
0.3%
258 1
 
0.3%
245 1
 
0.3%
312 1
 
0.3%
Other values (369) 369
97.4%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
379 1
0.3%
378 1
0.3%
377 1
0.3%
376 1
0.3%
375 1
0.3%
Distinct363
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 07:08:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:33.966941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:34.177433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.3
Real number (ℝ)

Missing 

Distinct370
Distinct (%)100.0%
Missing9
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean185.5
Minimum1
Maximum370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:34.405704image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.45
Q193.25
median185.5
Q3277.75
95-th percentile351.55
Maximum370
Range369
Interquartile range (IQR)184.5

Descriptive statistics

Standard deviation106.95404
Coefficient of variation (CV)0.5765716441
Kurtosis-1.2
Mean185.5
Median Absolute Deviation (MAD)92.5
Skewness0
Sum68635
Variance11439.16667
MonotonicityNot monotonic
2025-01-19T22:21:34.603844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1
 
0.3%
219 1
 
0.3%
228 1
 
0.3%
298 1
 
0.3%
237 1
 
0.3%
317 1
 
0.3%
175 1
 
0.3%
204 1
 
0.3%
288 1
 
0.3%
192 1
 
0.3%
Other values (360) 360
95.0%
(Missing) 9
 
2.4%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
370 1
0.3%
369 1
0.3%
368 1
0.3%
367 1
0.3%
366 1
0.3%
Distinct362
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 08:56:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:34.830248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:35.088250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.4
Real number (ℝ)

Distinct376
Distinct (%)100.0%
Missing3
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean188.5
Minimum1
Maximum376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:35.335469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.75
Q194.75
median188.5
Q3282.25
95-th percentile357.25
Maximum376
Range375
Interquartile range (IQR)187.5

Descriptive statistics

Standard deviation108.6860923
Coefficient of variation (CV)0.5765840442
Kurtosis-1.2
Mean188.5
Median Absolute Deviation (MAD)94
Skewness0
Sum70876
Variance11812.66667
MonotonicityNot monotonic
2025-01-19T22:21:35.521839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1
 
0.3%
191 1
 
0.3%
321 1
 
0.3%
204 1
 
0.3%
240 1
 
0.3%
297 1
 
0.3%
199 1
 
0.3%
272 1
 
0.3%
225 1
 
0.3%
296 1
 
0.3%
Other values (366) 366
96.6%
(Missing) 3
 
0.8%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
376 1
0.3%
375 1
0.3%
374 1
0.3%
373 1
0.3%
372 1
0.3%
Distinct359
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 09:15:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:35.761934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:35.963989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.5
Real number (ℝ)

Missing 

Distinct368
Distinct (%)100.0%
Missing11
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean184.5
Minimum1
Maximum368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:36.226689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.35
Q192.75
median184.5
Q3276.25
95-th percentile349.65
Maximum368
Range367
Interquartile range (IQR)183.5

Descriptive statistics

Standard deviation106.3766892
Coefficient of variation (CV)0.576567421
Kurtosis-1.2
Mean184.5
Median Absolute Deviation (MAD)92
Skewness0
Sum67896
Variance11316
MonotonicityNot monotonic
2025-01-19T22:21:36.415406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
300 1
 
0.3%
297 1
 
0.3%
183 1
 
0.3%
217 1
 
0.3%
280 1
 
0.3%
277 1
 
0.3%
148 1
 
0.3%
234 1
 
0.3%
321 1
 
0.3%
Other values (358) 358
94.5%
(Missing) 11
 
2.9%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
368 1
0.3%
367 1
0.3%
366 1
0.3%
365 1
0.3%
364 1
0.3%
Distinct188
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 10:14:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:36.657546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:36.875292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.6
Real number (ℝ)

Missing 

Distinct360
Distinct (%)100.0%
Missing19
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean180.5
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:37.072538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.95
Q190.75
median180.5
Q3270.25
95-th percentile342.05
Maximum360
Range359
Interquartile range (IQR)179.5

Descriptive statistics

Standard deviation104.0672859
Coefficient of variation (CV)0.5765500605
Kurtosis-1.2
Mean180.5
Median Absolute Deviation (MAD)90
Skewness0
Sum64980
Variance10830
MonotonicityNot monotonic
2025-01-19T22:21:37.314667image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
275 1
 
0.3%
145 1
 
0.3%
228 1
 
0.3%
315 1
 
0.3%
296 1
 
0.3%
177 1
 
0.3%
284 1
 
0.3%
243 1
 
0.3%
289 1
 
0.3%
256 1
 
0.3%
Other values (350) 350
92.3%
(Missing) 19
 
5.0%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
360 1
0.3%
359 1
0.3%
358 1
0.3%
357 1
0.3%
356 1
0.3%
Distinct353
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 11:05:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:37.509612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:37.723134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.7
Real number (ℝ)

Missing 

Distinct361
Distinct (%)100.0%
Missing18
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean181
Minimum1
Maximum361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:37.987139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q191
median181
Q3271
95-th percentile343
Maximum361
Range360
Interquartile range (IQR)180

Descriptive statistics

Standard deviation104.3559613
Coefficient of variation (CV)0.5765522726
Kurtosis-1.2
Mean181
Median Absolute Deviation (MAD)90
Skewness0
Sum65341
Variance10890.16667
MonotonicityNot monotonic
2025-01-19T22:21:38.184082image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
283 1
 
0.3%
281 1
 
0.3%
146 1
 
0.3%
220 1
 
0.3%
310 1
 
0.3%
301 1
 
0.3%
179 1
 
0.3%
278 1
 
0.3%
253 1
 
0.3%
Other values (351) 351
92.6%
(Missing) 18
 
4.7%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
361 1
0.3%
360 1
0.3%
359 1
0.3%
358 1
0.3%
357 1
0.3%
Distinct206
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 12:22:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:38.377859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:38.631409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.8
Real number (ℝ)

Missing 

Distinct362
Distinct (%)100.0%
Missing17
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean181.5
Minimum1
Maximum362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:38.806412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.05
Q191.25
median181.5
Q3271.75
95-th percentile343.95
Maximum362
Range361
Interquartile range (IQR)180.5

Descriptive statistics

Standard deviation104.6446367
Coefficient of variation (CV)0.5765544724
Kurtosis-1.2
Mean181.5
Median Absolute Deviation (MAD)90.5
Skewness0
Sum65703
Variance10950.5
MonotonicityNot monotonic
2025-01-19T22:21:38.991682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211 1
 
0.3%
289 1
 
0.3%
152 1
 
0.3%
216 1
 
0.3%
305 1
 
0.3%
304 1
 
0.3%
188 1
 
0.3%
270 1
 
0.3%
254 1
 
0.3%
279 1
 
0.3%
Other values (352) 352
92.9%
(Missing) 17
 
4.5%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
362 1
0.3%
361 1
0.3%
360 1
0.3%
359 1
0.3%
358 1
0.3%
Distinct362
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 14:20:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:39.198461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:39.447030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.9
Real number (ℝ)

Missing 

Distinct362
Distinct (%)100.0%
Missing17
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean181.5
Minimum1
Maximum362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:39.633924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.05
Q191.25
median181.5
Q3271.75
95-th percentile343.95
Maximum362
Range361
Interquartile range (IQR)180.5

Descriptive statistics

Standard deviation104.6446367
Coefficient of variation (CV)0.5765544724
Kurtosis-1.2
Mean181.5
Median Absolute Deviation (MAD)90.5
Skewness0
Sum65703
Variance10950.5
MonotonicityNot monotonic
2025-01-19T22:21:39.823729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
243 1
 
0.3%
299 1
 
0.3%
144 1
 
0.3%
216 1
 
0.3%
300 1
 
0.3%
310 1
 
0.3%
184 1
 
0.3%
260 1
 
0.3%
258 1
 
0.3%
303 1
 
0.3%
Other values (352) 352
92.9%
(Missing) 17
 
4.5%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
362 1
0.3%
361 1
0.3%
360 1
0.3%
359 1
0.3%
358 1
0.3%
Distinct228
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 16:34:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:40.015326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:40.264660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.10
Real number (ℝ)

Missing 

Distinct359
Distinct (%)100.0%
Missing20
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean180
Minimum1
Maximum359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:40.495609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.9
Q190.5
median180
Q3269.5
95-th percentile341.1
Maximum359
Range358
Interquartile range (IQR)179

Descriptive statistics

Standard deviation103.7786105
Coefficient of variation (CV)0.5765478362
Kurtosis-1.2
Mean180
Median Absolute Deviation (MAD)90
Skewness0
Sum64620
Variance10770
MonotonicityNot monotonic
2025-01-19T22:21:41.586436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299 1
 
0.3%
220 1
 
0.3%
293 1
 
0.3%
300 1
 
0.3%
176 1
 
0.3%
251 1
 
0.3%
256 1
 
0.3%
307 1
 
0.3%
262 1
 
0.3%
285 1
 
0.3%
Other values (349) 349
92.1%
(Missing) 20
 
5.3%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
359 1
0.3%
358 1
0.3%
357 1
0.3%
356 1
0.3%
355 1
0.3%
Distinct352
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 16:55:28
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:41.774083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:42.021465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.11
Real number (ℝ)

Missing 

Distinct356
Distinct (%)100.0%
Missing23
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean178.5
Minimum1
Maximum356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:42.231386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.75
Q189.75
median178.5
Q3267.25
95-th percentile338.25
Maximum356
Range355
Interquartile range (IQR)177.5

Descriptive statistics

Standard deviation102.9125843
Coefficient of variation (CV)0.5765410883
Kurtosis-1.2
Mean178.5
Median Absolute Deviation (MAD)89
Skewness0
Sum63546
Variance10591
MonotonicityNot monotonic
2025-01-19T22:21:42.417803image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178 1
 
0.3%
290 1
 
0.3%
299 1
 
0.3%
171 1
 
0.3%
241 1
 
0.3%
251 1
 
0.3%
306 1
 
0.3%
268 1
 
0.3%
289 1
 
0.3%
200 1
 
0.3%
Other values (346) 346
91.3%
(Missing) 23
 
6.1%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
356 1
0.3%
355 1
0.3%
354 1
0.3%
353 1
0.3%
352 1
0.3%
Distinct346
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 19:16:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:42.664304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:42.867951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.12
Real number (ℝ)

Missing 

Distinct350
Distinct (%)100.0%
Missing29
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean175.5
Minimum1
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:43.074390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.45
Q188.25
median175.5
Q3262.75
95-th percentile332.55
Maximum350
Range349
Interquartile range (IQR)174.5

Descriptive statistics

Standard deviation101.1805317
Coefficient of variation (CV)0.5765272463
Kurtosis-1.2
Mean175.5
Median Absolute Deviation (MAD)87.5
Skewness0
Sum61425
Variance10237.5
MonotonicityNot monotonic
2025-01-19T22:21:43.318425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
139 1
 
0.3%
239 1
 
0.3%
291 1
 
0.3%
266 1
 
0.3%
275 1
 
0.3%
192 1
 
0.3%
236 1
 
0.3%
203 1
 
0.3%
222 1
 
0.3%
Other values (340) 340
89.7%
(Missing) 29
 
7.7%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
350 1
0.3%
349 1
0.3%
348 1
0.3%
347 1
0.3%
346 1
0.3%
Distinct253
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 21:40:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:43.514819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:43.726742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.14
Real number (ℝ)

Missing 

Distinct345
Distinct (%)100.0%
Missing34
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean173
Minimum1
Maximum345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:43.991309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.2
Q187
median173
Q3259
95-th percentile327.8
Maximum345
Range344
Interquartile range (IQR)172

Descriptive statistics

Standard deviation99.73715456
Coefficient of variation (CV)0.5765153443
Kurtosis-1.2
Mean173
Median Absolute Deviation (MAD)86
Skewness0
Sum59685
Variance9947.5
MonotonicityNot monotonic
2025-01-19T22:21:44.185298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195 1
 
0.3%
238 1
 
0.3%
248 1
 
0.3%
217 1
 
0.3%
240 1
 
0.3%
222 1
 
0.3%
137 1
 
0.3%
246 1
 
0.3%
232 1
 
0.3%
239 1
 
0.3%
Other values (335) 335
88.4%
(Missing) 34
 
9.0%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
345 1
0.3%
344 1
0.3%
343 1
0.3%
342 1
0.3%
341 1
0.3%
Distinct249
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 22:45:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:44.376333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:44.555937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.15
Real number (ℝ)

Missing 

Distinct341
Distinct (%)100.0%
Missing38
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean171
Minimum1
Maximum341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:44.789840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q186
median171
Q3256
95-th percentile324
Maximum341
Range340
Interquartile range (IQR)170

Descriptive statistics

Standard deviation98.5824528
Coefficient of variation (CV)0.5765055719
Kurtosis-1.2
Mean171
Median Absolute Deviation (MAD)85
Skewness0
Sum58311
Variance9718.5
MonotonicityNot monotonic
2025-01-19T22:21:45.047002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
188 1
 
0.3%
227 1
 
0.3%
240 1
 
0.3%
222 1
 
0.3%
149 1
 
0.3%
241 1
 
0.3%
232 1
 
0.3%
204 1
 
0.3%
235 1
 
0.3%
Other values (331) 331
87.3%
(Missing) 38
 
10.0%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
341 1
0.3%
340 1
0.3%
339 1
0.3%
338 1
0.3%
337 1
0.3%
Distinct340
Distinct (%)90.4%
Missing3
Missing (%)0.8%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 23:50:36
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:45.285924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:45.499867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.16
Real number (ℝ)

Missing 

Distinct342
Distinct (%)100.0%
Missing37
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean171.5
Minimum1
Maximum342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:45.722525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.05
Q186.25
median171.5
Q3256.75
95-th percentile324.95
Maximum342
Range341
Interquartile range (IQR)170.5

Descriptive statistics

Standard deviation98.87112824
Coefficient of variation (CV)0.5765080364
Kurtosis-1.2
Mean171.5
Median Absolute Deviation (MAD)85.5
Skewness0
Sum58653
Variance9775.5
MonotonicityNot monotonic
2025-01-19T22:21:45.946204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
238 1
 
0.3%
252 1
 
0.3%
235 1
 
0.3%
240 1
 
0.3%
221 1
 
0.3%
194 1
 
0.3%
244 1
 
0.3%
228 1
 
0.3%
218 1
 
0.3%
183 1
 
0.3%
Other values (332) 332
87.6%
(Missing) 37
 
9.8%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
342 1
0.3%
341 1
0.3%
340 1
0.3%
339 1
0.3%
338 1
0.3%

Green Gate
Date

Missing 

Distinct249
Distinct (%)66.9%
Missing7
Missing (%)1.8%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 23:59:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:46.198975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:46.391497image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.17
Real number (ℝ)

Missing 

Distinct337
Distinct (%)100.0%
Missing42
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean169
Minimum1
Maximum337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:46.604446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.8
Q185
median169
Q3253
95-th percentile320.2
Maximum337
Range336
Interquartile range (IQR)168

Descriptive statistics

Standard deviation97.42775101
Coefficient of variation (CV)0.5764955681
Kurtosis-1.2
Mean169
Median Absolute Deviation (MAD)84
Skewness0
Sum56953
Variance9492.166667
MonotonicityNot monotonic
2025-01-19T22:21:46.829538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
214 1
 
0.3%
194 1
 
0.3%
241 1
 
0.3%
234 1
 
0.3%
225 1
 
0.3%
232 1
 
0.3%
185 1
 
0.3%
196 1
 
0.3%
184 1
 
0.3%
Other values (327) 327
86.3%
(Missing) 42
 
11.1%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
337 1
0.3%
336 1
0.3%
335 1
0.3%
334 1
0.3%
333 1
0.3%

Auburn Lake Trails
Date

Missing 

Distinct192
Distinct (%)68.3%
Missing98
Missing (%)25.9%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 23:55:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:47.030321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:47.270550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.18
Real number (ℝ)

Missing 

Distinct333
Distinct (%)100.0%
Missing46
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean167
Minimum1
Maximum333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:47.450374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.6
Q184
median167
Q3250
95-th percentile316.4
Maximum333
Range332
Interquartile range (IQR)166

Descriptive statistics

Standard deviation96.27304919
Coefficient of variation (CV)0.5764853245
Kurtosis-1.2
Mean167
Median Absolute Deviation (MAD)83
Skewness0
Sum55611
Variance9268.5
MonotonicityNot monotonic
2025-01-19T22:21:47.638299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
241 1
 
0.3%
235 1
 
0.3%
226 1
 
0.3%
233 1
 
0.3%
202 1
 
0.3%
198 1
 
0.3%
216 1
 
0.3%
186 1
 
0.3%
211 1
 
0.3%
Other values (323) 323
85.2%
(Missing) 46
 
12.1%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
333 1
0.3%
332 1
0.3%
331 1
0.3%
330 1
0.3%
329 1
0.3%

Quarry Road
Date

Missing 

Distinct155
Distinct (%)77.1%
Missing178
Missing (%)47.0%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 23:59:07
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:47.888198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:48.076779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.19
Real number (ℝ)

Missing 

Distinct333
Distinct (%)100.0%
Missing46
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean167
Minimum1
Maximum333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:48.251460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.6
Q184
median167
Q3250
95-th percentile316.4
Maximum333
Range332
Interquartile range (IQR)166

Descriptive statistics

Standard deviation96.27304919
Coefficient of variation (CV)0.5764853245
Kurtosis-1.2
Mean167
Median Absolute Deviation (MAD)83
Skewness0
Sum55611
Variance9268.5
MonotonicityNot monotonic
2025-01-19T22:21:48.515181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
235 1
 
0.3%
229 1
 
0.3%
238 1
 
0.3%
216 1
 
0.3%
201 1
 
0.3%
217 1
 
0.3%
196 1
 
0.3%
240 1
 
0.3%
215 1
 
0.3%
220 1
 
0.3%
Other values (323) 323
85.2%
(Missing) 46
 
12.1%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
333 1
0.3%
332 1
0.3%
331 1
0.3%
330 1
0.3%
329 1
0.3%

Pointed Rocks
Date

Missing 

Distinct130
Distinct (%)74.3%
Missing204
Missing (%)53.8%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 23:54:17
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:48.711375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:48.901529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.20
Real number (ℝ)

Missing 

Distinct333
Distinct (%)100.0%
Missing46
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean167
Minimum1
Maximum333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:49.080562image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.6
Q184
median167
Q3250
95-th percentile316.4
Maximum333
Range332
Interquartile range (IQR)166

Descriptive statistics

Standard deviation96.27304919
Coefficient of variation (CV)0.5764853245
Kurtosis-1.2
Mean167
Median Absolute Deviation (MAD)83
Skewness0
Sum55611
Variance9268.5
MonotonicityNot monotonic
2025-01-19T22:21:49.339828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
234 1
 
0.3%
232 1
 
0.3%
239 1
 
0.3%
218 1
 
0.3%
204 1
 
0.3%
209 1
 
0.3%
206 1
 
0.3%
235 1
 
0.3%
217 1
 
0.3%
216 1
 
0.3%
Other values (323) 323
85.2%
(Missing) 46
 
12.1%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
333 1
0.3%
332 1
0.3%
331 1
0.3%
330 1
0.3%
329 1
0.3%

Robie Point
Date

Missing 

Distinct112
Distinct (%)70.0%
Missing219
Missing (%)57.8%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 23:57:01
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:49.533691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:49.730211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.21
Real number (ℝ)

Missing 

Distinct330
Distinct (%)100.0%
Missing49
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean165.5
Minimum1
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:49.908541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.45
Q183.25
median165.5
Q3247.75
95-th percentile313.55
Maximum330
Range329
Interquartile range (IQR)164.5

Descriptive statistics

Standard deviation95.4070228
Coefficient of variation (CV)0.5764774792
Kurtosis-1.2
Mean165.5
Median Absolute Deviation (MAD)82.5
Skewness0
Sum54615
Variance9102.5
MonotonicityNot monotonic
2025-01-19T22:21:50.186192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
232 1
 
0.3%
226 1
 
0.3%
221 1
 
0.3%
216 1
 
0.3%
224 1
 
0.3%
225 1
 
0.3%
220 1
 
0.3%
218 1
 
0.3%
213 1
 
0.3%
Other values (320) 320
84.4%
(Missing) 49
 
12.9%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
330 1
0.3%
329 1
0.3%
328 1
0.3%
327 1
0.3%
326 1
0.3%

Finish
Date

Missing 

Distinct110
Distinct (%)68.8%
Missing219
Missing (%)57.8%
Memory size3.1 KiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 23:57:22
Invalid dates0
Invalid dates (%)0.0%
2025-01-19T22:21:50.491374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T22:21:50.791571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Position.22
Real number (ℝ)

Missing 

Distinct329
Distinct (%)100.0%
Missing50
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean165
Minimum1
Maximum329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:51.011148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.4
Q183
median165
Q3247
95-th percentile312.6
Maximum329
Range328
Interquartile range (IQR)164

Descriptive statistics

Standard deviation95.11834734
Coefficient of variation (CV)0.5764748323
Kurtosis-1.2
Mean165
Median Absolute Deviation (MAD)82
Skewness0
Sum54285
Variance9047.5
MonotonicityStrictly increasing
2025-01-19T22:21:51.250502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
227 1
 
0.3%
225 1
 
0.3%
224 1
 
0.3%
223 1
 
0.3%
222 1
 
0.3%
221 1
 
0.3%
220 1
 
0.3%
219 1
 
0.3%
218 1
 
0.3%
217 1
 
0.3%
Other values (319) 319
84.2%
(Missing) 50
 
13.2%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
ValueCountFrequency (%)
329 1
0.3%
328 1
0.3%
327 1
0.3%
326 1
0.3%
325 1
0.3%

Finish_time
Text

Missing 

Distinct110
Distinct (%)68.8%
Missing219
Missing (%)57.8%
Memory size3.1 KiB
2025-01-19T22:21:51.608393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1280
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)67.5%

Sample

1st row14:40:22
2nd row15:04:09
3rd row15:09:16
4th row15:19:42
5th row15:22:15
ValueCountFrequency (%)
00:00:00 50
31.2%
22:46:24 2
 
1.2%
17:43:34 1
 
0.6%
17:40:11 1
 
0.6%
17:46:34 1
 
0.6%
15:09:16 1
 
0.6%
15:19:42 1
 
0.6%
15:22:15 1
 
0.6%
15:29:33 1
 
0.6%
15:37:02 1
 
0.6%
Other values (100) 100
62.5%
2025-01-19T22:21:52.534051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 366
28.6%
: 320
25.0%
2 152
11.9%
3 103
 
8.0%
1 96
 
7.5%
5 68
 
5.3%
4 60
 
4.7%
9 31
 
2.4%
6 30
 
2.3%
7 28
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 366
28.6%
: 320
25.0%
2 152
11.9%
3 103
 
8.0%
1 96
 
7.5%
5 68
 
5.3%
4 60
 
4.7%
9 31
 
2.4%
6 30
 
2.3%
7 28
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 366
28.6%
: 320
25.0%
2 152
11.9%
3 103
 
8.0%
1 96
 
7.5%
5 68
 
5.3%
4 60
 
4.7%
9 31
 
2.4%
6 30
 
2.3%
7 28
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 366
28.6%
: 320
25.0%
2 152
11.9%
3 103
 
8.0%
1 96
 
7.5%
5 68
 
5.3%
4 60
 
4.7%
9 31
 
2.4%
6 30
 
2.3%
7 28
 
2.2%
Distinct367
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:52.848762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3032
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique355 ?
Unique (%)93.7%

Sample

1st row01:51:37
2nd row01:50:57
3rd row01:51:48
4th row01:51:55
5th row01:51:26
ValueCountFrequency (%)
02:29:13 2
 
0.5%
03:29:02 2
 
0.5%
02:44:02 2
 
0.5%
02:52:38 2
 
0.5%
02:28:06 2
 
0.5%
03:10:22 2
 
0.5%
02:12:17 2
 
0.5%
02:29:07 2
 
0.5%
02:35:26 2
 
0.5%
02:53:57 2
 
0.5%
Other values (357) 359
94.7%
2025-01-19T22:21:53.349307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 758
25.0%
0 579
19.1%
2 457
15.1%
3 308
10.2%
5 234
 
7.7%
1 209
 
6.9%
4 188
 
6.2%
9 79
 
2.6%
6 79
 
2.6%
8 75
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 758
25.0%
0 579
19.1%
2 457
15.1%
3 308
10.2%
5 234
 
7.7%
1 209
 
6.9%
4 188
 
6.2%
9 79
 
2.6%
6 79
 
2.6%
8 75
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 758
25.0%
0 579
19.1%
2 457
15.1%
3 308
10.2%
5 234
 
7.7%
1 209
 
6.9%
4 188
 
6.2%
9 79
 
2.6%
6 79
 
2.6%
8 75
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 758
25.0%
0 579
19.1%
2 457
15.1%
3 308
10.2%
5 234
 
7.7%
1 209
 
6.9%
4 188
 
6.2%
9 79
 
2.6%
6 79
 
2.6%
8 75
 
2.5%
Distinct359
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:53.666089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3032
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique351 ?
Unique (%)92.6%

Sample

1st row04:45:27
2nd row04:48:34
3rd row04:47:59
4th row04:45:47
5th row04:51:14
ValueCountFrequency (%)
00:00:00 11
 
2.9%
09:15:00 5
 
1.3%
05:35:49 2
 
0.5%
07:56:56 2
 
0.5%
06:39:15 2
 
0.5%
06:29:47 2
 
0.5%
07:47:59 2
 
0.5%
07:46:05 2
 
0.5%
04:53:52 1
 
0.3%
05:03:21 1
 
0.3%
Other values (349) 349
92.1%
2025-01-19T22:21:54.215620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 758
25.0%
0 636
21.0%
4 240
 
7.9%
5 228
 
7.5%
7 216
 
7.1%
3 187
 
6.2%
2 179
 
5.9%
8 177
 
5.8%
1 174
 
5.7%
6 142
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 758
25.0%
0 636
21.0%
4 240
 
7.9%
5 228
 
7.5%
7 216
 
7.1%
3 187
 
6.2%
2 179
 
5.9%
8 177
 
5.8%
1 174
 
5.7%
6 142
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 758
25.0%
0 636
21.0%
4 240
 
7.9%
5 228
 
7.5%
7 216
 
7.1%
3 187
 
6.2%
2 179
 
5.9%
8 177
 
5.8%
1 174
 
5.7%
6 142
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 758
25.0%
0 636
21.0%
4 240
 
7.9%
5 228
 
7.5%
7 216
 
7.1%
3 187
 
6.2%
2 179
 
5.9%
8 177
 
5.8%
1 174
 
5.7%
6 142
 
4.7%
Distinct362
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:54.476049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3032
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique360 ?
Unique (%)95.0%

Sample

1st row07:03:37
2nd row07:09:28
3rd row07:07:31
4th row07:07:15
5th row07:12:24
ValueCountFrequency (%)
00:00:00 17
 
4.5%
14:20:00 2
 
0.5%
10:14:26 1
 
0.3%
08:29:08 1
 
0.3%
07:07:31 1
 
0.3%
07:07:15 1
 
0.3%
07:12:24 1
 
0.3%
07:36:33 1
 
0.3%
07:23:22 1
 
0.3%
07:20:07 1
 
0.3%
Other values (352) 352
92.9%
2025-01-19T22:21:54.896602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 758
25.0%
1 567
18.7%
0 430
14.2%
2 290
 
9.6%
3 252
 
8.3%
4 206
 
6.8%
5 188
 
6.2%
7 112
 
3.7%
8 87
 
2.9%
9 81
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 758
25.0%
1 567
18.7%
0 430
14.2%
2 290
 
9.6%
3 252
 
8.3%
4 206
 
6.8%
5 188
 
6.2%
7 112
 
3.7%
8 87
 
2.9%
9 81
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 758
25.0%
1 567
18.7%
0 430
14.2%
2 290
 
9.6%
3 252
 
8.3%
4 206
 
6.8%
5 188
 
6.2%
7 112
 
3.7%
8 87
 
2.9%
9 81
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 758
25.0%
1 567
18.7%
0 430
14.2%
2 290
 
9.6%
3 252
 
8.3%
4 206
 
6.8%
5 188
 
6.2%
7 112
 
3.7%
8 87
 
2.9%
9 81
 
2.7%
Distinct346
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:55.195896image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3032
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique340 ?
Unique (%)89.7%

Sample

1st row09:05:03
2nd row09:16:05
3rd row09:18:13
4th row09:18:53
5th row09:32:47
ValueCountFrequency (%)
00:00:00 29
 
7.7%
15:54:23 2
 
0.5%
18:00:00 2
 
0.5%
16:35:22 2
 
0.5%
14:23:04 2
 
0.5%
16:25:55 2
 
0.5%
11:54:33 1
 
0.3%
09:18:13 1
 
0.3%
09:18:53 1
 
0.3%
09:32:47 1
 
0.3%
Other values (336) 336
88.7%
2025-01-19T22:21:55.643145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 758
25.0%
1 517
17.1%
0 406
13.4%
3 241
 
7.9%
4 236
 
7.8%
5 222
 
7.3%
2 216
 
7.1%
6 143
 
4.7%
7 119
 
3.9%
8 98
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 758
25.0%
1 517
17.1%
0 406
13.4%
3 241
 
7.9%
4 236
 
7.8%
5 222
 
7.3%
2 216
 
7.1%
6 143
 
4.7%
7 119
 
3.9%
8 98
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 758
25.0%
1 517
17.1%
0 406
13.4%
3 241
 
7.9%
4 236
 
7.8%
5 222
 
7.3%
2 216
 
7.1%
6 143
 
4.7%
7 119
 
3.9%
8 98
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 758
25.0%
1 517
17.1%
0 406
13.4%
3 241
 
7.9%
4 236
 
7.8%
5 222
 
7.3%
2 216
 
7.1%
6 143
 
4.7%
7 119
 
3.9%
8 98
 
3.2%
Distinct249
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2025-01-19T22:21:55.954054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3032
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)45.1%

Sample

1st row10:36:00
2nd row10:51:00
3rd row10:53:00
4th row10:59:00
5th row11:13:00
ValueCountFrequency (%)
00:00:00 38
 
10.0%
20:40:00 4
 
1.1%
20:57:00 3
 
0.8%
15:50:00 3
 
0.8%
21:29:00 3
 
0.8%
20:56:00 3
 
0.8%
20:43:00 3
 
0.8%
16:42:00 3
 
0.8%
21:05:00 3
 
0.8%
17:36:00 3
 
0.8%
Other values (239) 313
82.6%
2025-01-19T22:21:56.504257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1073
35.4%
: 758
25.0%
1 381
 
12.6%
2 211
 
7.0%
3 111
 
3.7%
4 103
 
3.4%
5 94
 
3.1%
9 93
 
3.1%
8 74
 
2.4%
7 68
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1073
35.4%
: 758
25.0%
1 381
 
12.6%
2 211
 
7.0%
3 111
 
3.7%
4 103
 
3.4%
5 94
 
3.1%
9 93
 
3.1%
8 74
 
2.4%
7 68
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1073
35.4%
: 758
25.0%
1 381
 
12.6%
2 211
 
7.0%
3 111
 
3.7%
4 103
 
3.4%
5 94
 
3.1%
9 93
 
3.1%
8 74
 
2.4%
7 68
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1073
35.4%
: 758
25.0%
1 381
 
12.6%
2 211
 
7.0%
3 111
 
3.7%
4 103
 
3.4%
5 94
 
3.1%
9 93
 
3.1%
8 74
 
2.4%
7 68
 
2.2%

Robie Point_time
Text

Missing 

Distinct112
Distinct (%)70.0%
Missing219
Missing (%)57.8%
Memory size3.1 KiB
2025-01-19T22:21:56.833349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1280
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)69.4%

Sample

1st row14:28:00
2nd row14:51:00
3rd row14:56:00
4th row15:09:00
5th row15:10:00
ValueCountFrequency (%)
00:00:00 49
30.6%
22:39:44 1
 
0.6%
14:56:00 1
 
0.6%
15:09:00 1
 
0.6%
15:10:00 1
 
0.6%
15:17:00 1
 
0.6%
15:26:51 1
 
0.6%
15:27:03 1
 
0.6%
15:29:48 1
 
0.6%
15:42:42 1
 
0.6%
Other values (102) 102
63.7%
2025-01-19T22:21:57.319446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 368
28.7%
: 320
25.0%
2 154
12.0%
1 105
 
8.2%
3 92
 
7.2%
5 70
 
5.5%
4 59
 
4.6%
7 35
 
2.7%
6 29
 
2.3%
8 25
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 368
28.7%
: 320
25.0%
2 154
12.0%
1 105
 
8.2%
3 92
 
7.2%
5 70
 
5.5%
4 59
 
4.6%
7 35
 
2.7%
6 29
 
2.3%
8 25
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 368
28.7%
: 320
25.0%
2 154
12.0%
1 105
 
8.2%
3 92
 
7.2%
5 70
 
5.5%
4 59
 
4.6%
7 35
 
2.7%
6 29
 
2.3%
8 25
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 368
28.7%
: 320
25.0%
2 154
12.0%
1 105
 
8.2%
3 92
 
7.2%
5 70
 
5.5%
4 59
 
4.6%
7 35
 
2.7%
6 29
 
2.3%
8 25
 
2.0%

Lyon Ridge_GAP
Real number (ℝ)

Distinct367
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3233.796834
Minimum-362
Maximum9256
Zeros1
Zeros (%)0.3%
Negative6
Negative (%)1.6%
Memory size3.1 KiB
2025-01-19T22:21:57.537292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-362
5-th percentile282
Q12259
median3250
Q34319.5
95-th percentile5463
Maximum9256
Range9618
Interquartile range (IQR)2060.5

Descriptive statistics

Standard deviation1538.125415
Coefficient of variation (CV)0.4756407078
Kurtosis0.618823417
Mean3233.796834
Median Absolute Deviation (MAD)1000
Skewness0.1039098718
Sum1225609
Variance2365829.792
MonotonicityNot monotonic
2025-01-19T22:21:57.736647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2256 2
 
0.5%
1240 2
 
0.5%
-11 2
 
0.5%
3740 2
 
0.5%
2629 2
 
0.5%
2248 2
 
0.5%
2250 2
 
0.5%
5845 2
 
0.5%
4725 2
 
0.5%
2189 2
 
0.5%
Other values (357) 359
94.7%
ValueCountFrequency (%)
-362 1
0.3%
-40 1
0.3%
-11 2
0.5%
-2 1
0.3%
-1 1
0.3%
ValueCountFrequency (%)
9256 1
0.3%
7927 1
0.3%
7666 1
0.3%
7569 1
0.3%
7555 1
0.3%

Robinson Flat_GAP
Real number (ℝ)

Distinct359
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8838.986807
Minimum-17127
Maximum16173
Zeros1
Zeros (%)0.3%
Negative11
Negative (%)2.9%
Memory size3.1 KiB
2025-01-19T22:21:57.905321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-17127
5-th percentile339.5
Q17194
median10073
Q312301
95-th percentile14928.4
Maximum16173
Range33300
Interquartile range (IQR)5107

Descriptive statistics

Standard deviation5907.391918
Coefficient of variation (CV)0.668333605
Kurtosis8.498116088
Mean8838.986807
Median Absolute Deviation (MAD)2596
Skewness-2.495172012
Sum3349976
Variance34897279.27
MonotonicityNot monotonic
2025-01-19T22:21:58.158931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-17127 11
 
2.9%
16173 5
 
1.3%
3022 2
 
0.5%
11489 2
 
0.5%
6828 2
 
0.5%
6260 2
 
0.5%
10952 2
 
0.5%
10838 2
 
0.5%
13959 1
 
0.3%
10319 1
 
0.3%
Other values (349) 349
92.1%
ValueCountFrequency (%)
-17127 11
2.9%
0 1
 
0.3%
12 1
 
0.3%
20 1
 
0.3%
152 1
 
0.3%
ValueCountFrequency (%)
16173 5
1.3%
15861 1
 
0.3%
15860 1
 
0.3%
15801 1
 
0.3%
15764 1
 
0.3%

Devil's Thumb_GAP
Real number (ℝ)

Distinct362
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13772.11082
Minimum-25417
Maximum26183
Zeros1
Zeros (%)0.3%
Negative18
Negative (%)4.7%
Memory size3.1 KiB
2025-01-19T22:21:58.410017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-25417
5-th percentile196.2
Q111258.5
median16286
Q319987.5
95-th percentile23567.9
Maximum26183
Range51600
Interquartile range (IQR)8729

Descriptive statistics

Standard deviation10431.20998
Coefficient of variation (CV)0.7574154841
Kurtosis6.385023145
Mean13772.11082
Median Absolute Deviation (MAD)4123
Skewness-2.35947575
Sum5219630
Variance108810141.7
MonotonicityNot monotonic
2025-01-19T22:21:58.745180image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-25417 17
 
4.5%
26183 2
 
0.5%
20493 1
 
0.3%
15064 1
 
0.3%
18224 1
 
0.3%
21419 1
 
0.3%
21815 1
 
0.3%
16837 1
 
0.3%
19685 1
 
0.3%
19670 1
 
0.3%
Other values (352) 352
92.9%
ValueCountFrequency (%)
-25417 17
4.5%
-2 1
 
0.3%
0 1
 
0.3%
218 1
 
0.3%
234 1
 
0.3%
ValueCountFrequency (%)
26183 2
0.5%
25883 1
0.3%
25186 1
0.3%
25129 1
0.3%
25023 1
0.3%

Foresthill_GAP
Real number (ℝ)

Distinct346
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18034.44327
Minimum-32703
Maximum36657
Zeros1
Zeros (%)0.3%
Negative29
Negative (%)7.7%
Memory size3.1 KiB
2025-01-19T22:21:58.950617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-32703
5-th percentile-32703
Q115584.5
median22961
Q328566.5
95-th percentile32736.5
Maximum36657
Range69360
Interquartile range (IQR)12982

Descriptive statistics

Standard deviation16756.35672
Coefficient of variation (CV)0.9291308009
Kurtosis3.711138982
Mean18034.44327
Median Absolute Deviation (MAD)6251
Skewness-2.041463602
Sum6835054
Variance280775490.6
MonotonicityNot monotonic
2025-01-19T22:21:59.211988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-32703 29
 
7.7%
26452 2
 
0.5%
24560 2
 
0.5%
32097 2
 
0.5%
27019 2
 
0.5%
19081 2
 
0.5%
21335 1
 
0.3%
30184 1
 
0.3%
29157 1
 
0.3%
29466 1
 
0.3%
Other values (336) 336
88.7%
ValueCountFrequency (%)
-32703 29
7.7%
0 1
 
0.3%
41 1
 
0.3%
662 1
 
0.3%
790 1
 
0.3%
ValueCountFrequency (%)
36657 1
0.3%
36597 1
0.3%
36057 1
0.3%
34852 1
0.3%
34808 1
0.3%

Ford's Bar (Cal-3)_GAP
Real number (ℝ)

Distinct249
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20693.98417
Minimum-38160
Maximum43740
Zeros1
Zeros (%)0.3%
Negative38
Negative (%)10.0%
Memory size3.1 KiB
2025-01-19T22:21:59.438369image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-38160
5-th percentile-38160
Q117550
median28440
Q334800
95-th percentile39732
Maximum43740
Range81900
Interquartile range (IQR)17250

Descriptive statistics

Standard deviation22034.99256
Coefficient of variation (CV)1.064801847
Kurtosis2.329595105
Mean20693.98417
Median Absolute Deviation (MAD)7800
Skewness-1.798871266
Sum7843020
Variance485540897
MonotonicityNot monotonic
2025-01-19T22:21:59.696161image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-38160 38
 
10.0%
36240 4
 
1.1%
32700 3
 
0.8%
40020 3
 
0.8%
40080 3
 
0.8%
32460 3
 
0.8%
35520 3
 
0.8%
30120 3
 
0.8%
25200 3
 
0.8%
37260 3
 
0.8%
Other values (239) 313
82.6%
ValueCountFrequency (%)
-38160 38
10.0%
0 1
 
0.3%
900 1
 
0.3%
960 1
 
0.3%
1020 1
 
0.3%
ValueCountFrequency (%)
43740 1
0.3%
43380 1
0.3%
43140 1
0.3%
42660 1
0.3%
42300 1
0.3%

Robie Point_GAP
Real number (ℝ)

Missing 

Distinct112
Distinct (%)70.0%
Missing219
Missing (%)57.8%
Infinite0
Infinite (%)0.0%
Mean-592.23125
Minimum-52080
Maximum34141
Zeros1
Zeros (%)0.3%
Negative49
Negative (%)12.9%
Memory size3.1 KiB
2025-01-19T22:22:00.103397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-52080
5-th percentile-52080
Q1-52080
median14977.5
Q329369.5
95-th percentile32498.65
Maximum34141
Range86221
Interquartile range (IQR)81449.5

Descriptive statistics

Standard deviation35387.76312
Coefficient of variation (CV)-59.75328576
Kurtosis-1.368824877
Mean-592.23125
Median Absolute Deviation (MAD)15412
Skewness-0.6700038295
Sum-94757
Variance1252293779
MonotonicityNot monotonic
2025-01-19T22:22:00.475400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-52080 49
 
12.9%
29504 1
 
0.3%
30900 1
 
0.3%
30924 1
 
0.3%
30389 1
 
0.3%
30347 1
 
0.3%
30266 1
 
0.3%
30390 1
 
0.3%
30276 1
 
0.3%
30232 1
 
0.3%
Other values (102) 102
26.9%
(Missing) 219
57.8%
ValueCountFrequency (%)
-52080 49
12.9%
0 1
 
0.3%
1380 1
 
0.3%
1680 1
 
0.3%
2460 1
 
0.3%
ValueCountFrequency (%)
34141 1
0.3%
33291 1
0.3%
33171 1
0.3%
33087 1
0.3%
32939 1
0.3%

Finish_GAP
Real number (ℝ)

Missing 

Distinct110
Distinct (%)68.8%
Missing219
Missing (%)57.8%
Infinite0
Infinite (%)0.0%
Mean-1229.375
Minimum-52822
Maximum33420
Zeros1
Zeros (%)0.3%
Negative50
Negative (%)13.2%
Memory size3.1 KiB
2025-01-19T22:22:00.765626image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-52822
5-th percentile-52822
Q1-52822
median14312
Q329364.75
95-th percentile32830.2
Maximum33420
Range86242
Interquartile range (IQR)82186.75

Descriptive statistics

Standard deviation35957.00174
Coefficient of variation (CV)-29.24819664
Kurtosis-1.410639605
Mean-1229.375
Median Absolute Deviation (MAD)16484
Skewness-0.6427958026
Sum-196700
Variance1292905974
MonotonicityNot monotonic
2025-01-19T22:22:01.012965image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-52822 50
 
13.2%
29162 2
 
0.5%
29643 1
 
0.3%
31141 1
 
0.3%
31111 1
 
0.3%
30835 1
 
0.3%
30757 1
 
0.3%
30586 1
 
0.3%
30550 1
 
0.3%
30480 1
 
0.3%
Other values (100) 100
26.4%
(Missing) 219
57.8%
ValueCountFrequency (%)
-52822 50
13.2%
0 1
 
0.3%
1427 1
 
0.3%
1734 1
 
0.3%
2360 1
 
0.3%
ValueCountFrequency (%)
33420 1
0.3%
33358 1
0.3%
33172 1
0.3%
33119 1
0.3%
33086 1
0.3%